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Genetic Algorithm-Based Optimized Gabor Filters for Content-Based Image Retrieval

  • D. Madhavi
  • M. Ramesh Patnaik
Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 624)

Abstract

Fast and exact searching of digital image from the large database is the great demand. In this paper, a hybrid technique to improve the efficiency of content-based image retrieval (CBIR) is proposed. It uses combination of color, texture, and shape feature extraction methods. Color features are extracted using HSV histograms. For texture feature extraction, instead of traditional Gabor filter, four Gabor filters are simultaneously tuned in the desired direction using genetic algorithm and features are extracted in each direction simultaneously. The shape features are obtained using shape signature function with polygonal fitting algorithm. By the sequential process of these three stages, the retrieval performance is greatly improved. The simulation results prove that the proposed analysis gives significant improvement with respect to retrieval performance and computational complexity with the other proposed schemes.

Keywords

Gabor filter Genetic algorithm Image retrieval Precision Recall 
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References

  1. 1.
    Yang, W.X: An Effective image retrieval scheme using color, texture & shape features, computer Standards & Interfaces, vol. 33, Issue 1, pp. 59–68. (2011)Google Scholar
  2. 2.
    T. Kato: Database architecture for content-based image retrieval. In: Proceedings of the SPIE—The International Society for Optical Engineering, vol. 1662, pp. 112–113. (1992)Google Scholar
  3. 3.
    Zhenhua Zhang: An Improving Technique of Color Histogram in Segmentation-based Image Retrieval. In: Fifth International Conference on Information Assurance and Security, pp. 381–384. IEEE Computer Society (2009)Google Scholar
  4. 4.
    Huang, P.W., Dai, S.K: Image retrieval by texture similarity. Pattern Recognition, Vol. 36, pp. 665–679. (2003)Google Scholar
  5. 5.
    Krishna, S., Balasubramanian, V., Black, J., Sethuraman, P: Person-Specific Characteristic Feature Selection for Face Recognition. Biometrics: Theory, Methods, and Applications Wiely publications. (2009)Google Scholar
  6. 6.
    Shrivastava N: An efficient technique for retrieval of color images in large databases. Dept. of Computer Science and Engineering, Jaypee University of Engineering and Technology, Raghogarh, Guna 473226, India Google Scholar
  7. 7.
    Tsai, D. M., Lin, C. P., Huang, K.T: Defect Detection in Colored Texture Surfaces using Gabor Filters. The Imaging Science Journal. vol. 53, pp. 27–37. (2005)Google Scholar
  8. 8.
    Chisti, K. M., Srinivas, K. S., Prasad G: 2D Gabor filter for surface defect detection using GA and PSO optimization techniques. AMSE Journals. vol. 58, pp. 67–83. (2015)Google Scholar
  9. 9.
    Manjunath, B., Ma, W: Texture features for Browsing and retrieval of image data. IEEE transactions on pattern analysis and machine intelligence, vol. 18. No.8, pp. 837–842. (1996)Google Scholar
  10. 10.
    Madhavi, D., Patnaik, M.R: Image retrieval using GA optimized Gabor filter. Indian Journal of Science and Technology, vol. 9(44), pp. 1–11. (2016)Google Scholar
  11. 11.
    Hu, R.X: Angular Pattern and Binary Angular Pattern for Shape Retrieval., IEEE Transactions on Image Processing, vol. 23, No. 3, pp. 1118–1127. (2014)Google Scholar
  12. 12.
    Jhanwar, N: Content based Image Retrieval using Motif Cooccurrence matrix. Image and Vision Computing, vol. 22, pp. 1211–1220. (2004)Google Scholar
  13. 13.

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of ECE, GITGITAM UniversityVisakhapatnamIndia
  2. 2.Department of Instrument Technology, College of EngineeringAndhra UniversityVisakhapatnamIndia

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